Comments (3)
Sorry for the late reply, no idea why I didn't get the notification by email...
Thanks for the feedback. If I understand it correctly, you mean the grid size should be [20, 20, 20] but the float-point issue makes it to be [20, 21, 20]?
If that is the case, here is my considerations: in the general case, the size of the bounding box would not be the multiple of grid size / search radius. Then we don't have this kind of issues. Even if this happens, I guess both the computational overhead and the memory overhead is marginal.
Maybe I should change the data type of grid_params
to be double? Do you have any suggestion?
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I think the "round" operation seems to be more reasonable rather than "int" or "floor"
from frnn.
That's a good idea. But if we round down, the search radius can be larger than cell_size * int_ratio, which means we need to search one more layer of grids (e.g. originally we search over a 5x5x5 grid, now with this round-down, we need to search over 7x7x7). According to my experiment results, this would be much slower if the search radius is not the exact multiple of the cell_size.
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Related Issues (15)
- GPU memory cache HOT 1
- What is the function of the grid points? Can it be used to subsample a point cloud in a voxel downsample way? HOT 3
- [Environmental configuration] Installation of frnn HOT 2
- error: command 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\Tools\\MSVC\\14.37.32822\\bin\\HostX86\\x64\\link.exe' failed with exit code 1120 HOT 4
- Is there a version of Windows available HOT 1
- installation issue HOT 1
- when i install frnn, happen some errors HOT 1
- Issue building prefix_sum HOT 2
- What are the limits on dimension and K? HOT 22
- CUDA OOM(potential enhancement) HOT 5
- changes to frnn.py doesn't take effect HOT 4
- Weird OOM in frnn_gather HOT 3
- Assertion `cell_idx < G` failed HOT 1
- Import _C error HOT 9
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